This study confirms the important role of nonfarm participation in improving household
income in peri-urban areas. This suggests that government policies aiming at improving
household income should focus on promoting rural nonfarm activities and improving
households' access to these activities. Nevertheless, access to nonfarm employment in Hanoi's
sub-urban areas has been found to be determined by a number of factors such as education,
access to formal credit, a prime location for doing nonfarm businesses (Tuyen and Huong,
2013; Tuyen and Lim, 2011), access to local markets (Ngoc, 2004) and the development of
local infrastructure (Nguyen, 2009). As a result, policy intervention in these factors can help
peri-urban households increase their income by providing them with favourable conditions to
participate intensively in nonfarm activities.
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ews, coffee and pepper. As a result, in Vietnam's rural areas, which
represent three-quarters of the total population and most of the poor, agricultural production is
the main living for more than half of the total workforce (WB, 2011b). Therefore, the State's
farmland acquisition has a major effect on households in Vietnam's rural and peri-urban
areas. From 2003-2008, it was estimated that the acquisition of agricultural land considerably
affected the livelihood of 950,000 farmers in 627,000 farm households. About 25-30 percent
of these farmers became jobless or had unstable jobs and 53 percent of the households suffered
from a decline in income (VietNamNet/TN, 2009).
Land acquisition directly and indirectly affects household livelihoods by creating new
non-farm employment opportunities and livelihood asset changes, respectively. However,
apart from a number of rural households who attain benefits from this process because such
households have enough resources or take full advantage of urbanization to obtain better
livelihoods, many other households have become jobless, vulnerable and have precarious
livelihoods even after receiving a significant amount of money as compensation for their
land loss. Some case studies in peri-urban areas of Hanoi reveal mixed impacts of farmland
acquisition on local people’s livelihoods. When investigating a case study in a peri-urban
village of Hanoi where two thirds of agricultural land was lost due to urbanization between
1998 and 2007, Nguyen (2009) found that many households benefited from their proximity
to universities and urban centres. Income from renting out boarding houses to students and
migrant workers emerged as the most important income source for the majority of households.
However, a number of households faced insecure livelihoods because they did not have rooms
for renting out and many landless farmers became jobless, particularly elderly and poorly
educated farmers. In another case study in a peri-urban village of Hanoi, Do (2006) found that
while farmland acquisition caused a loss of farm jobs, food supply and agricultural income
sources, many households actively adapted to the new circumstance by diversifying their
labour in manual labour jobs. Consequently, a high but unstable income from casual wage
work became the main income source for many households.
Using secondary data gathered from various published documents in Vietnam, Nguyen,
McGrath, and Pamela (2006) found that over the previous decade, Vietnam had experienced
rapid urbanization and industrialization in peri-urban areas. One outcome of this process
was that a large number of rural households had lost their farmland for the development of
industrial zones and urban areas, and many among them had fallen in poverty. Moreover,
Tran Quang Tuyen and Vu Van Huong 341
the results from a large-scale survey in eight developed cities and provinces with the highest
level of farmland loss provided a quite detailed picture of both positive and negative effects
of farmland acquisition on household income (Le, 2007). On average, while almost half of
households suffered from a significant decline in farm income, more than half reported that
their nonfarm income sources increased considerably after losing land. Regarding the total
income that households earned after land loss, 25 percent obtained a higher level, while 44.5
percent maintained the same level and 30.5 percent experienced a decline (Le, 2007). In a case
study in urbanizing areas of Hung Yen Province, Nguyen, Nguyen, and Ho (2013) found that
although a large proportion of households have changed their livelihoods towards nonfarm
activities and had a much higher level of income than before losing land, there have been many
other households whose income was unchanged or declined after losing land.
The main objective of this study is to answer the key research question: how, and to what
extent, has farmland loss affected household income and its components in Hanoi' sub-urban
areas, Vietnam. Our motivation to pursue this topic stems from two main reasons. First,
although there have been many studies examining the impacts of land loss on household
income and its sources, their findings are mixed. Second, all above studies used qualitative
methods or descriptive statistics for investigating these impacts and this clearly restricts our
understanding. Using a unique dataset from a 2010 household survey and econometric tools,
this paper has made a significant contribution to the literature by providing the first econometric
evidence of the impacts of land loss on household income and its components. Our results
showed that while the loss of farmland in both years (2008 and 2009) had no impact on total
household income, it had a positive effect on nonfarm income and other income but a negative
effect on farm income. These findings suggest that the effects of land loss on different income
components might balance each other.
2. DATA AND METHODS
2.1. Research site
Hoai Duc sub-urban district of Hanoi was selected for this study. This is because among
the districts of Hanoi, Hoai Duc holds the biggest number of land acquisition projects with
a huge area of farmland having been converted for nonfarm uses in recent years (Huu Hoa,
2011). Hoai Duc is located on the northwest side of Hanoi City, about 20 km from the Central
Business District (see Appendix 1). The district is situated in a very prime location, surrounded
by a number of important roads, namely Thang Long highway (the country’s biggest and most
modern highway) and National Way 32, and is in close proximity to new industrial zones,
new urban areas and Bao Son Paradise Park (the biggest entertainment and tourism complex
in North Vietnam). In the period 2006-2010, around 1,560 hectares of agricultural land were
compulsorily acquired by the State for 85 projects in the district (LH, 2010), leading to a
significant decrease in the size of farmland per households in Hoai Duc. The average size of
farmland per household in the district was about 840 m2 in 2009 (Hoai Duc District People's
Committee, 2010a) which was much lower than that in Ha Tay Province (1,975m2) and that of
other provinces (7,600 m2) in 2008 (Central Institue for Economic Managment [CIEM], 2009).
Prior to being merged into Hanoi on 1st August 2008, Hoai Duc was a district of Ha Tay
342 The Impact of Land Loss on Household Income: The Case of Hanoi's Sub-Urban Areas, Vietnam
Province, a neighbouring province of Hanoi Capital. The district is covered with 8,247
hectares of land, of which farmland accounts for 4,272 hectares, 91 percent of whichis used
by households and individuals (Hoai Duc District People's Committee, 2010a). There are 20
administrative units in the district, consisting of 19 communes and one town. Hoai Duc has
around 50,400 households with a population of 193,600 people. Prior to its transfer to Hanoi,
Hoai Duc was the richest district in Ha Tay Province (Nguyen, 2007). In 2009, Hoai Duc
GDP per capita reached 15 million Vietnam dong (VND) per year (Hoai Duc District People's
Committee, 2010b), which was less than half of Hanoi’s average (32 million VND per year)
(Vietnam Government Web Portal, 2010).2
2.2. Data collection
Adapted from General Statistical Office [GSO] (2006), we designed a household questionnaire
to collect quantitative data on household characteristics, assets and income sources. A sample
of 480 households from 6 communes, including 80 households (40 with land loss and 40
without land loss) from each commune, was randomly chosen.3 To achieve the target sample
size of 480 households, therefore, 600 households were selected, including 120 reserves.
A disproportionate stratified sampling method was implemented with two stages: first, 12
communes that had farmland acquisition were clustered into three groups based on their
employment structure. The first group was three agricultural communes; the second one
included five communes that had a combination of both agricultural and non-agricultural
production while the third one was characterized by four non-agricultural communes. From
each group, two communes were randomly selected. Then, from each of these communes, 100
households (50 with land loss and 50 without land loss) including 20 reserves (10 with land
loss and 10 without land loss) were randomly chosen using Circular Systematic Sampling
(Groves, Fowler, Couper, Lepkowski, & Singer, 2009).
The data were collected between the beginning of April and the end of June 2010 by means
of face-to-face interviews with the head of a household in the presence of other household
members. In total, 477 households were successfully interviewed, among which 237
households lost their farmland at different levels. Some lost little, some lost part and others
lost most or all of their land.4 Their farmland was compulsorily acquired by the government
for a number of projects relating to the enlargement and improvement of Thang Long highway,
the construction of industrial clusters, new urban areas and other non-farm use purposes (Ha
Tay Province People's Committee, 2008). Due to some delays in the implementation of land
acquisition, of the 237 households, 124 households had farmland acquired in the first half
of 2008 and 113 households had farmland acquired in early 2009. In the remainder of this
paper, households whose farmland was lost partly or totally by the State's compulsory land
acquisition will be referred to as "land-losing households".
2 1 USD equated to about 18,000 VND in 2009.
3 Six selected communes are Song Phuong, Lai Yen, Kim Chung, An Thuong, Duc Thuong and Van Con.
4 Statistic summary of the area of acquired farmland is available in Appendix 2.
Tran Quang Tuyen and Vu Van Huong 343
2.3. Analytical models
First, the household sample was split into two groups, namely land-losing and non-land-losing
households. Statistical analyses were then used to compare the mean of household assets and
household income between the two groups. According to Gujarati and Porter (2009), there
is a variety of statistical techniques for investigating the differences in two or more mean
values, which are referred to as analysis of variance. However, a similar objective can be
achieved by using the framework of regression analysis. Therefore, regression analysis using
Analysis of Variance (ANOVA) model was applied to explore the differences in the mean of
household assets and income between the two groups of households. In addition, a chi-square
test was employed to determine whether a statistically significant association existed between
two categorical variables such as the type of households (land-losing and non-land-losing
households) and their participation in nonfarm activities.
Because total household income is continuously distributed over positive values, ordinary
least squares (OLS) regression was used to examine factors affecting total household income.
However, other components of household income, including farm income, nonfarm income
and other income, are continuous but censored at zero. In this case, the OLS estimator will give
biased results and Tobit regression is usually used for such data (Atamanov & van den Berg,
2012). Therefore, Tobit regression was applied to examine the determinants of farm income,
nonfarm income and other income. Household income and its components were assumed to
be determined by household characteristics and assets. In addition, other factors, in this case
the loss of farmland and the participation by households in nonfarm activities before farmland
acquisition were included as regressors in the models. Finally, commune dummy variables
were also included in the models to control for fixed commune effects. Thus, we have the
following equations for the models:
Yi = ϕ0 + ϕ1Xi + ϕ2Zi + ϕ3NPi + ϕ4LLi + ϕ5Di + ui
Sij = β0 + β1Xi + β2Zi + β3NPi + β4LLi + β5Di + εi
where Yi is the total income of a household i and Sij is the income source j of the household
I that were assumed to be determined by the household's characteristics ( Xi ) and assets
( Zi ), farmland loss ( LLi ), past nonfarm participation ( NPi ) and commune dummy
( Di ) (the commune in which the household I lived). The definition and measurements of
variables included in the models are displayed in Table 1.
The vectors of household characteristics (Xi) include household size, dependency ratio, age of
and gender of household head, age and education of working age members. The justification
of including these variables is as follow. Larger size households might be indicative of labour
availability and therefore were expected to obtain a higher level of total income, farm and
nonfarm income. However, households with a higher dependency ratio might be indicative of
labour shortage and were hypothesized to earn a lower level of total income, farm and nonfarm
income. Having more working members who are male might be an advantage, which in turn
might allow households to earn more income, including both farm and nonfarm income.
344 The Impact of Land Loss on Household Income: The Case of Hanoi's Sub-Urban Areas, Vietnam
Table 1: Definition and measurements of variables included in the models
Definition Measurement
Independent variables
Total income Total annual income from farm, nonfarm and other income Natural log
Nonfarm income Total annual income from wage and self-employment in 1,000 VND
nonfarm activities
Farm income Total annual income from planting and livestock 1,000 VND
production and other related activities
Other income Total annual income from other sources 1,000 VND
Explanatory variables
Farmland loss
Land loss 2009 The proportion of farmland that was compulsorily Ratio
acquired by the State in 2008.
Land loss 2008 The proportion of farmland that was compulsorily Ratio
acquired by the State in 2008.
Household characteristics
Household size Total household members. Number
Dependency ratio This ratio is calculated by the number of household Ratio
members aged under 15 years and over 59 years,
divided by the number of household members aged
15-59 years.
Age of household head Age of household head. Years
Gender of household head Whether or not the household head is male. Male=1; Female=0
Age of working members Average age of members aged 15 and over who were Years
employed in the last 12 months
Education of working Average years of formal schooling of members aged Years
age members 15 and over who were employed in the last 12 months
Household assets
Farmland per capita The size of owned farmland per capita 100 m2
House location Whether or not households have a house or a plot of Dummy
residential land in a prime location. (=1 if yes)
Residential land size The size of residential land owned by households 10 m2
Productive assets Total value of productive assets. Natural log
Past nonfarm Whether or not the household had participated in Dummy variable
participation nonfarm activities before farmland acquisition.
Commune variables The commune in which the household resided Dummy variable
( Lai Yen Commune is the base group)
Households with working members that had more years of formal schooling were expected to
earn a higher amount of nonfarm and total income. However, the income effect of the age of
working members might be ambiguous. Younger working members might have more chances
to take up nonfarm jobs, which in turn might generate more nonfarm income and therefore
result in households having more total income. Nevertheless, older members tend to have
more work experience and thus might have access to lucrative job opportunities, which allows
them to earn a higher level of total income.
Tran Quang Tuyen and Vu Van Huong 345
Regarding to assets (Zi ),owing more productive assets was expected to increase total
household income as well as its components. Within the context of urban and sub-urban areas
in Vietnam, a house or a plot of residential land has become an important resource, as
households use them as productive assets. An area of several tens of square meters of
residential land can be enough for a household to build a house for rent (Nguyen, 2009). In
addition, a house or a residential land plot in a prime location such as the main road of a village
can be used for opening a shop (Nguyen, Vu, and Lebailly, 2011; Nguyen, 2009).5 Therefore,
we included the size of residential land and the location of houses (or of residential land plots)
as explanatory variables in the models. Households with larger size of residential land or a
house in prime location were expected to earn a higher amount of nonfarm income and total
income.
Nonfarm participation ( NPi ) was found to be a determinant of household welfare in Vietnam
rural (Pham, Bui, and Dao, 2010; Van de Walle and Cratty, 2004). Nevertheless, the inclusion
of nonfarm participation as an explanatory variable in the model is likely to suffer from the
potential endogeneity (Van de Walle and Cratty, 2004). This is because nonfarm participation
was determined by household characteristics, assets and other exogenous factors. However, in
our case study, the households' nonfarm participation in the past (before farmland acquisition)
was predetermined and treated as an exogenous variable.6 Therefore, we included a dummy
variable of past nonfarm participation in the models as an explanatory variable. Households
with past nonfarm participation were hypothesized to earn a higher amount of nonfarm income
and total income than those without past nonfarm participation.
In the present study, the loss of farmland ( LLi ) is an exogenous variable, resulting from
the State's compulsory farmland acquisition.7 The State conducted the farmland acquisition
at two different times; therefore, land-losing households were divided into two groups: (i)
those that had farmland acquired in 2008 and (ii) those did in 2009. The rationale for this
division was that difference in lengths of time since farmland acquisition was expected to have
different effects on household income and its components. In addition, the level of farmland
loss, as noted, was quite different among households. Therefore, this factor, as measured by
the proportion of farmland acquired by the State in 2008 and in 2009, was used as the variable
of interest. Households with more land loss were expected to earn more nonfarm income
because the loss of farmland might induce households to intensively participate in nonfarm
activities. However, households with more land loss were expected to earn a lower amount of
farm income due to land shortage. The discussion suggests that the impact of land loss on total
income might be positive if the extra income earned from nonfarm activities is greater than
the amount of lost farm income. Conversely, the impact might be negative if the amount of
5 A prime location is defined as: the location of house or the location of a plot of residential land situated on the main road of a village
or at the crossroads or very close to local markets or to industrial zones, and to a high way or new urban areas. Such locations enable
households to use their house for opening a shop, or a workshop or for renting.zones, hi-tech parks, urban and residential areas and
projects in the highest investment fund group (WB, 2011a).
6 According to Kennedy (2003), lagged values of endogenous variables are predetermined and treated as exogenous variables,
because they are given constants for determination of the current time period's values of the endogenous variables.
7 As noted by Wooldridge (2013), an exogenous event is often a change in the State's policy that affects the environment in which
individuals and households operate.
346 The Impact of Land Loss on Household Income: The Case of Hanoi's Sub-Urban Areas, Vietnam
additional nonfarm income cannot compensate for the loss of farm income. Another possibility
is that land loss might have virtually no impact on total income at all if its effects on farm
income and nonfarm income balance each other.
3. RESULTS AND DISCUSSION
3.1. Background on household assets and income
Table 2 provides some information about household characteristics, assets and income for the
household sample. There were no statistical significant differences in the size of households,
dependency ratio, gender of household heads and number of male working members between
the two groups. On average, the age of the household heads for all the surveyed households
was 51 and the corresponding age among the land-losing households was approximately 4
years older than that among the non-land-losing households. The average age of working
members among the group of land-losing households was about 3 years older than that in the
group of non-land-losing households, while the disparity in average years of schooling was
negligible between the two groups.
Table 2: Summary statistics of household characteristics, assets and income
All households Land-losing Non-land-losing t-value/
Variables households households Pearson
a
Mean SD Mean SD Mean SD chi2
Household characteristics/assets
Household size 4.49 1.61 4.46 1.73 4.50 1.55 -0.25
Dependency ratio (%) 60.58 66.78 54.54 68.68 64.00 65.60 -1.27
Gender of household 0.77 0.42 0.77 0.42 0.77 0.42 0.022
head (1=male)
Age of household head 51.21 12.34 53.95 12.04 49.60 12.24 3.44***
Male working members 1.25 0.70 1.23 0.72 1.26 0.70 -0.40
Average age of working 40.46 8.25 42.24 8.51 39.48 7.95 3.88***
members
Average schooling years of 8.37 2.90 8.24 2.58 8.44 3.07 -1.87*
working members
Farmland size 266.20 230.37 155.40 129.71 330.85 251.01 -8.12***
per capita (m2)
Residential land 218.76 146.16 230.34 151.52 211.94 142.71 1.19
per household (m2)
Proportion of houses in a 0.32 0.47 0.25 0.44 0.36 0.48 1.723
prime location
Total value of productive 22,081 20,089 18,397 17,377 24,252 21,261 -2.87***
assets
Past nonfarm participation 0.78 0.41 0.73 0.44 0.81 0.40 4.367**
Tran Quang Tuyen and Vu Van Huong 347
Table 2: Summary statistics of household characteristics, assets and income (cont)
All households Land-losing Non-land-losing t-value/
Variables households households Pearson
a
Mean SD Mean SD Mean SD chi2
Household income
Total annual household income 60,642 30,034 54,154 25,725 60,465 36,171 -3.22***
Monthly household income 1,126 591 1,012 487 1,193 635 -2.88***
per capita
Total annual farm income 14,432 16,169 11,564 15,452 16,121 16,368 -2.79***
Total annual nonfarm income 46,211 35,391 42,590 26,938 48,344 39,431 -1.69*
Total annual other income 3,409 8,676 3,454 7,461 3,382 9,331 0.09
Number of households 477 237 240
Notes: a applied to dummy variables. Productive assets, household income and its components measured
in VND. 1 USD equated to about 18,000 VND in 2009. Means and standard deviations (SD) are adjusted
for sampling weights. *, **, ** * mean statistically significant at 10%, 5 % and 1 %, respectively.
The data in Table 2 indicate that the distribution of farmland was quite unequal between the
two groups of households. On average, non-land-losing households owned approximately
twice as much farmland per capita as land-losing households did. However, there were no
statistically significant differences between the two groups in the size of residential land and
proportion of households with a house in a prime location. The non-land-losing households
possessed a higher total value of productive assets than their counterpart and this difference
is statistically significant. The results show that a statistically significant association existed
between the type of households and their past nonfarm participation; while 81 percent of
the non-land-losing households had participated in nonfarm activities before the farmland
acquisition, the corresponding figures for the land-losing households were 73 percent.
Non-land-losing households earned a higher amount of farm income, nonfarm income and
total income than land-losing households. Possibly this suggests that land loss might have
had a negative effect on total income and its components. However, the dummy variable of
land loss simply indicates the difference in the total income and its sources, if it exists, but it
does not suggest the causes of this difference. Differences in households' educational levels,
productive assets, the size of residential land, the prime location of house and past nonfarm
participation may all have a considerable effect on the income difference. Therefore, other
variables that potentially affect household income had been taken into account in multiple
regression models, which will be presented in the subsequent section.
3.2. Determinants of total household income and its sources
Table 3 reports the results from Tobit estimates for determinants of household income
components. It is evident that many explanatory variables are highly statistically significant,
with their signs as expected. The results indicate that land loss has a positive effect on nonfarm
income but a negative effect on farm income. A 10 percentage point-increase in the land loss
in 2009 corresponds with an increase of around 1.2 million VND in nonfarm income, holding
348 The Impact of Land Loss on Household Income: The Case of Hanoi's Sub-Urban Areas, Vietnam
all other variables constant. A similar increase in the land loss in 2009 resulted in a decrease
of about 1.4 million VND in farm income. In addition, the land loss in 2009 had a positive
effect on other income. These results indicate that the loss of farmland had different effects
on household income components. Also, this suggests that the loss of farmland had motivated
households to participate intensively in nonfarm activities as a way to supplement their income
with nonfarm income sources. In overall, the findings support the previous survey findings
obtained by Le (2007), who found that after losing land, households' farm income considerably
decreased but their nonfarm incomes significantly increased.
The result reveals that having more family members increased the amount of farm income.
This indicates that farming is a more labour-intensive strategy than nonfarm activities.
Possibly, this reflects the fact that having more family labour allowed many households to
intensively cultivate vegetables that are more profitable than rice but also require a greater
labour input.8 A similar picture was also observed in Thanh Tri, a sub-urban district of Hanoi
(van den Berg, Van Wijk, and Van Hoi, 2003), and on the peripheries of Ho Chi Minh City
(Jansen, Midmore, Binh, Valasayya, and Tru, 1996). Having more male working members
also allowed households to earn more nonfarm income but less other income. Female headed
households were more likely to earn more farm income than male headed households,
suggesting that farming was more suitable for women than men. Younger working members
tended to earn more nonfarm income but less farm income. Higher levels of education of
working members enabled households to earn a higher amount of nonfarm income and a lower
amount of farm income. This suggests that better education might shift households away from
farming. In general, these findings are similar to those in Shandong Province, China where
working members with younger age and better education were more likely to participate in
off-farm activities (Huang, Wu, and Rozelle, 2009).
Regarding the role of household assets in income-generation, the results show that households
with a house in a prime location were more likely to earn a much higher amount of nonfarm
income as compared to those without this advantage. This is because households have utilized
their houses for nonfarm activities such as opening a shop, a workshop or a small restaurant.
This finding suggests that a house (or a plot of residential land) in a prime locationwas
important to the livelihoods of peri-urban households. Holding a higher value of productive
assets is positively associated with a higher amount of both nonfarm and farm income. Finally,
households' past nonfarm participation is closely linked to their current income sources.
Households with past nonfarm participation earned a much higher amount of nonfarm income
(about 26 billion VND) and a much lower amount of farm income (about 11 billion VND) as
compared to those without past nonfarm participation, holding all other variables constant.
8 In some places of Hoai Duc District, the mean net return per year per hectare for fresh vegetable production is between 3-4 times
higher than for rice. The vegetable cultivation has short durations; about 40-60 days (depending on types of vegetables), which
allows farmers to harvest 5-6 crops per year (Tùng, 2010). Therefore, vegetable production requires a higher labour input than rice.
Tran Quang Tuyen and Vu Van Huong 349
Table 3: Tobit estimates for determinants of income sources
Explanatory variables Nonfarm income Farm income Other income
Land loss 2008 10,441.15* -21,624.70*** 3,920.98
(5,423.7205) (3,441.04) (4,189.36)
Land loss 2009 12,185.3725** -14,278.28*** 7,537.46**
(5,635.6445) (3,428.76) (3,454.07)
Household size -75.477 2,868.171*** -1,045.828
(1,317.321) (678.652) (958.648)
Dependency ratio -1,288.11 -3,645.62** 3,197.16**
(2,478.79) (1,604.75) (1,534.23)
Number of male working members 6,040.34** 254.81 -6,415.86***
(2,870.24) (1,114.45) (1,974.43)
Household head's gender -5,594.95 3,614.78* -544.84
(4,014.55) (1,981.74) (2,469.71)
Household head's age 313.05* -145.98* 360.79***
(174.01) (80.66) (101.35)
Age of working members -522.81** 447.94*** 107.00
(224.97) (127.17) (147.22)
Education of working members 4,857.78*** -1,055.13*** 1,819.48***
(855.42) (345.67) (493.68)
House location 9,968.5845** -5,710.58*** -460.66
(4,077.67) (1,878.57) (2,141.84)
Residential land 118.23 106.31 9.71
(115.84) (65.20) (69.06)
Productive assets 5,306.54*** 2,693.17*** 447.21
(1,406.09) (750.22) (853.73)
Past nonfarm participation 26,200.82*** -10,908.62*** 3,633.90
(4,494.29) (2,091.46) (2,938.39)
Song Phuong 8,630.80* 4,801.55* -1,640.71
(4,868.83) (2,697.27) (3,311.82)
Kim Chung 17,889.23*** -8,267.667*** 4,648.17
(4,482.76) (2,408.11) (3,081.16)
An Thuong -445.2056 4,618.09* -2,234.55
(5,032.8506) (2,554.68) (3,356.18)
Duc Thuong -119.9856 5,293.68** -569.89
(4,097.97) (2,643.05) (3,092.35)
Van Con 5,985.84 1,201.24 2,839.32
(5,178.62) (3,142.83) (3,341.50)
Constant -77,008.11*** -18,027.53* -44,664.88***
(17,992.37) (9,185.15) (12,306.96)
Sigma 27,805.24*** 14,245.70*** 14,388.68***
(1,865.10) (905.21) (1,077.47)
Pseudo R2 0.0276 0.0260 0.0336
Observations 460 460 460
Notes: Robust standard errors in parentheses. Estimates are adjusted for sampling weights. *,**,***
mean statistically significant at 10%, 5%, and 1%, respectively.
350 The Impact of Land Loss on Household Income: The Case of Hanoi's Sub-Urban Areas, Vietnam
Table 4 reports the results from OLS estimates for determinants of total household income.
Surprisingly, the coefficients on the land loss variables in both years are not statistically
significant; indicating that land loss had no impact on total household income. As expected,
the different effects of land loss on various income sources might have balanced each other.
The estimation results from Tobit model suggest that the amount of farm income lost due to
farmland loss might have been compensated by the additional income earned from nonfarm
activities. These results, therefore, can help explain why the loss of farmland had no negative
effect on household income in the short-term. In addition, they suggest that farmland loss
Table 4: Ordinary least squares estimates for determinants of total household income
(Log of annual total household income)
Explanatory variables Coefficient SE
Farmland loss
Land loss 2008 -0.05 (0.071)
Land loss 2009 0.03 (0.081)
Household characteristics
Household size 0.05*** (0.017)
Dependency ratio -0.05 (0.035)
Number of male working members 0.05 (0.032)
Household head's gender 0.04 (0.052)
Household head's age 0.00 (0.002)
Age of working members -0.00 (0.003)
Education of working members 0.05*** (0.010)
Household assets
House location 0.04 (0.045)
Residential land size 0.00 (0.002)
Productive assets 0.14*** (0.020)
Past nonfarm participation 0.17*** (0.051)
Commune dummy
Song Phuong 0.15** (0.071)
Kim Chung 0.19*** (0.065)
An Thuong 0.07 (0.063)
Duc Thuong 0.09 (0.060)
Van Con 0.14* (0.075)
Constant 8.56*** (0.257)
Prob> F 0.0000
R-squared 0.4789
Observations 460
Notes: Robust standard errors (SE) are in parentheses. Estimates are adjusted for
sampling weights. *,**,*** mean statistically significant at 10%, 5%, and 1%,
respectively.
Tran Quang Tuyen and Vu Van Huong 351
might have a positive effect on household income in the long-term as households have more
time to change their livelihoods towards lucrative nonfarm activities. This explanation is
well supported by the recent survey findings obtained by Nguyen et al. (2013), who found
that ten years after losing land, the majority of households with higher levels of land loss
had higher rates of job change and their income per capita wasapproximately seven times
higher as compared to before losing land. The above finding simply that farmland has been
gradually lost its crucial role in peri-urban livelihoods and its role has been gradually replaced
by nonfarm employment.9
Holding all other variables constant, an additional family member is associated with five
percent greater total income. In addition, a one year increase in formal education of working
member corresponds with five percent greater total income. Among various types of
household assets, only productive assets have a positive association with total income. The
elasticity of total income to higher values of productive assets is 0.14. In overall, the above
findings are in line with the previous findings in Vietnam rural obtained by Nguyen, Kant,
and MacLaren (2004) who found that having more family members, better education and
more productive assets all had a positive effect on household income. Finally, households with
past participation in nonfarm activities earned an amount of total income 18.5 percent higher
than those without past nonfarm participation.10 This finding is partly in accordance with that
of Do et al. (2001) who found that nonfarm households were much more likely to belong to
the high income groups than farm households in rural Vietnam.
4. CONCLUSION AND POLICY IMPLICATIONS
The impacts of farmland loss (due to urbanization and industrialization) on household income
and its sources were investigated in previous studies using qualitative analysis or descriptive
statistics. Going beyond the literature, we have quantified this relationship by using a novel
dataset from a 2010 household survey and econometric methods. Although land-losing
households earned a lower level of total income than non-land-losing households, the results
of multiple regression analyses show that the one and two-year effects of farmland loss on
total household income are not statistically significant. These results confirm that the loss of
farmland had no negative effect on household income in Hanoi's peri-urban areas in the short-
term. This can be explained by the fact that the loss of farm income might have been offset
by the amount of income earned from nonfarm activities. These arguments are well supported
by our econometric findings which indicate that land loss has a positive effect on nonfarm
income and other income but a negative effect on farm income. Therefore, a possible policy
implication here is that the loss of farm land should be seen as a positive factor since it can
make rural household livelihoods more diversified and secure by motivating households to
participate intensively in nonfarm activities. The econometric findings in Vietnam and several
9 We also examined the impact of farmland on household income sources and the regression results in Appendix 3 show that
farmland has a negative effect on nonfarm income, suggesting that land-limited households tend to participate intensively in
nonfarm activities.
10 Because the dependent variable (total household income) is in logarithmic form, households with past nonfarm participation are
predicted to have total income that is higher about 18.5% [ exp (0.17) -1=0.1853] than those without past nonfarm participation.
352 The Impact of Land Loss on Household Income: The Case of Hanoi's Sub-Urban Areas, Vietnam
developing countries also showed that land limited households were more likely to engage
in nonfarm activities and thus leads rural households to pursue this way of enhancing their
wellbeing (Winters et al., 2009).
The results show that some asset-related variables, including education, productive assets
and a house with a prime location, all had a positive effect on nonfarm income. This implies
that governmental support for households' access to formal credit can help them have more
financial resources and accumulate more productive assets, these, in turn, allow themto
participate in nonfarm activities. Encouraging parental investment in their children's education
should be considered a way to move out of farming and to seize lucrative nonfarm job
opportunities for the next generation.
This study confirms the important role of nonfarm participation in improving household
income in peri-urban areas. This suggests that government policies aiming at improving
household income should focus on promoting rural nonfarm activities and improving
households' access to these activities. Nevertheless, access to nonfarm employment in Hanoi's
sub-urban areas has been found to be determined by a number of factors such as education,
access to formal credit, a prime location for doing nonfarm businesses (Tuyen and Huong,
2013; Tuyen and Lim, 2011), access to local markets (Ngoc, 2004) and the development of
local infrastructure (Nguyen, 2009). As a result, policy intervention in these factors can help
peri-urban households increase their income by providing them with favourable conditions to
participate intensively in nonfarm activities.
ACKNOWLEDGMENTS
The authors thank Vietnam National University, Hanoi for funding the publication of this
paper. The authors also would like to thank colleagues for helpful feedback on earlier draft
versions of this paper.
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356 The Impact of Land Loss on Household Income: The Case of Hanoi's Sub-Urban Areas, Vietnam
APPENDICES
Appendix 1: Location of Hoai Duc peri-urban district
Tran Quang Tuyen and Vu Van Huong 357
Appendix 2: Loss of and decline in farmland size among land-losing households
Mean SD Min Max Mean SD
The area of acquired farmland in 744 389 24 1,880 767 394
2009 (m2/household). N =113
Proportion of farmland loss
(%/household). N =113 56.50 25.40 1.96 100 58.00 25.00
The area of acquired farmland in 765 435 120 2,520 709 390
2008 (m2/household). N=124
Proportion of farmland loss
(%/household). N=124 54.00 24.00 12.20 100 54.23 24.40
Farmland size before losing land
(m2/household). N =237 1,484 706 280 4,860 1,430 658
Current farmland size
(m2/household). N=237 729 599 0 3,600 693 556
Note: SD: standard deviation. Estimates in the last two columns are adjusted for sampling weights.
358 The Impact of Land Loss on Household Income: The Case of Hanoi's Sub-Urban Areas, Vietnam
Appendix 3: Tobit estimates of the impact of farmland on household income sources
Nonfarm income Farm income Other income
Household size -608.71 3,930.97*** -955.48
(1,376.62) (688.48) (934.34)
Dependency ratio -1,955.87 -2,161.58 3,004.78*
(2,503.93) (1,565.65) (1,555.39)
Number of male working members 6,134.04** 64.54 -6,498.44***
(2,859.12) (1,031.84) (1,962.66)
Household head's gender -5,257.48 2,793.42 -450.85
(3,936.83) (1,729.67) (2,462.27)
Household head's age 380.47** -254.00*** 374.47***
(174.39) (75.29) (101.78)
Age of working members -438.45* 313.60** 129.32
(236.93) (132.29) (151.10)
Education of working members 4,813.47*** -941.61*** 1,799.40***
(863.00) (324.26) (493.37)
Farmland per capita -1,614.11* 3,081.19*** -78.93
(908.95) (571.44) (710.75)
House location 9,692.19** -4,650.85*** -271.74
(4,125.24) (1,743.23) (2,126.57)
Residential land 167.90 27.17 21.78
(116.37) (56.59) (65.80)
Log of total value of productive assets 5,477.82*** 2,327.93*** 358.10
(1,426.77) (735.77) (873.06)
Past nonfarm participation 24,623.74*** -8,029.98*** 3,516.82
(4,598.04) (2,302.19) (2,987.75)
Song Phuong 7,748.70* 4,883.98** -2,641.68
(4,684.16) (2,406.22) (3,202.51)
Kim Chung 15,365.70*** -4,698.87** 3,402.52
(4,447.74) (2,342.06) (3,131.73)
An Thuong 1,424.47 -1,378.34 -3,426.75
(4,759.405) (2,334.47) (3,415.71)
Duc Thuong -473.88 5,833.99** -61.45
(4,122.17) (2,543.59) (3,112.17)
Van Con 4,357.01 2,156.65 898.13
(5,141.19) (2,903.22) (3,395.30)
Constant -75,038.08*** -22,180.53** -43,376.55***
(18,071.49) (8,849.48) (12,095.53)
Sigma 27,817.30*** 13,551.93*** 14,412.08***
(1,877.888) (903.102) (1,070.826)
Pseudo R2 0.0275 0.0276 0.0300
Observations 460 460 460
Notes: Robust standard errors in parentheses. Estimates are adjusted for sampling weights. *,**,***
mean statistically significant at 10%, 5%, and 1%, respectively.
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